Agents require training data for RL. This data is rare in comparison to what we feed foundation models, and Google is sitting on a dragon's hoard of sensor and tracking data. I'm not counting them out yet.
Minor quibbles: Hawking radiation has a blackbody spectrum, so we get a variety of wavelengths, with a peak determined by the size of the hole. The outgoing particles should eventually include any kind of particle that can be created in the Standard Model, not just photons and gravitons.
Strangely, it is _not_ the instrinsic curvature of spacetime that produces Hawking radiation. There's a variation on Hawking radiation -- called Unruh radiation -- experienced by accelerated observers in flat spacetime. Any two observers will agree on the value of a field (e.g. the electric field) at a point, but an accelerated observer will experience empty spacetime as a _thermal bath of particles_, with a temperature proportional to the acceleration. Relatively accelerated observers don't agree on what the vacuum is.
I don't think the distinction between holography and string theory is as clean as you're making it. The vague idea of holography predates string theory, but most of the working examples we have of holography come from string theory. That's what AdS/CFT is: It's an explicitly holographic description of superstring theory in an anti-Desitter space. It's frankly bizarre that the author of this article doesn't mention that fact. I suspect we are seeing a popular science rebranding of string theory without the word strings.
I didn't intend to assert a clean distinction, you're exactly right about its connection to string theory. What I mean is that String Theory Writ Large enjoyed a lot of popular esteem in the 00s and 2010s in public communication as being our potential new framework for what lies beneath the quantum world, but string theory is huge in and of itself.
It's more a difference of emphasis (holography in particular) than something that's outside the scope of string theory.
Most research isn't useful. Research on average is extremely useful. Great ideas are rare, and we don't know in advance where they are.
If you do know, let me know and I'll lead a round for your VC fund. I for one would never have predicted that abstract topology would lead to nuclear weapons within a few generations.
And conceptually novel approaches to outstanding problems are the sort of thing that a retrain should pick up on, because they would be hard to compress into what it already knows.
Those notations are used when writing down the models, because they make clear the intrinsic geometry, the basic symmetries, etc. But they're not used so much in the study of solutions to the equations. Solutions tend to have peculiar features, tend to break underlying symmetries, etc. and there only needs to be one nasty particular solution to prove the NS conjecture wrong.
Frenkel does a nice job explaining the Langlands program in general. But Buzzard's complaint about Langlands, I believe, refers specifically to the proof of a version of the Geometric Langlands Conjecture by Gaitsgory et al. The proo f is of order thousand pages of mathematical text and builds off of thousands of pages of higher-categorical algebraic geometry by Lurie & others. It's a ripe target for formalization because it's terrifically complicated, not well understood or thoroughly digested yet, and relatively important. A formal proof would be reassuring to mathematicians, whereas Fermat's Last Theorem is relatively unique in that so many mathematicians have examined the proof that it's not very likely to be wrong.
Do we have any examples of an current day AI system introducing a novel concept or perspective. We've got plenty of counterexamples discovered and some theorems proven, but afaik nothing analogous to a new definition.
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